Machine Learning Engineer 4 – Python, AWS, SQL, GenAI

Capital One

. Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams .

Posted 9/21/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying Machine Learning models and solutions, utilizing programming languages such as Python and Java, and leveraging cloud platforms like AWS, GCP, or Azure. Proficient in building and optimizing data pipelines, applying best practices in software development, and managing large-scale distributed systems.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud Deployment (AWS, GCP, Azure)Kubernetes ManagementData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Tools & Technologies

KubernetesCI/CDAgile MethodologyCloud PlatformsData Pipelines

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree (Preferred)

Industry Keywords

Machine Learning TechniquesModel EvaluationHyperparameter TuningResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate within a cross-functional Agile team to create and enhance software enabling big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring
Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java
Support Marketing and Messaging platforms delivering hyper-personalized omnichannel messages and experiences

Requirements

What you’ll need

Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience programming with Python, Java, Golang, or C++
At least 4 years of Machine Learning experience using PyTorch or TensorFlow and libraries including Pandas, NumPy, and Scikit-learn
At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI/ML data
At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure)
At least 2 years of experience using Kubernetes to manage large-scale containerized ML software systems
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred)
3+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred)
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD (preferred)
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans (preferred)
3+ years of experience with Machine Learning techniques, model types, model architectures, training concepts, and model evaluation and diagnosis (preferred)
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred)
1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation (preferred)
Authored/co-authored a paper on an ML technique, model, or proof of concept (preferred)
Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial and other benefits supporting total well-being
Reasonable accommodations for applicants who require them